The Role of Mobile Applications in the Doctor’s Working Time Management System
Bibliographic record
Abstract
Background: Information technologies have become a mandatory component for health care institutions, as well as for doctors. Doctors’ use of medical mobile applications to access medical information has the potential to improve the use of their working time. Primary care physicians can use mobile applications to communicate with their patients, thereby improving the health of individual patients and the population as a whole. Objectives: The aim of the study is to determine the effectiveness of the use of medical reference mobile applications by general practitioners in their medical practice. Methods: The research was conducted through a sociological survey and a questionnaire survey. Descriptive statistics were used for the analysis. Results and Conclusions: The study found that in Ukraine, medical mobile applications are mainly reference, and their use allows doctors to improve decision-making and has a positive effect on the level of health of the population and the doctor’s working time management. The assessment of the duration of the appointment using applications showed that in most cases, the time of the consultation reduced to 15 minutes, which testifies to the improvement of this type of assistance to the population. Besides, the use of applications provides a significant reduction of the time for making a clinical decision by 5 minutes. We found a relationship between the average duration of the appointment, as well as the time for making a clinical decision and the frequency of using the mobile application during the working day. The hypothesis of a direct effect of an innovative approach (use of a mobile application) on the time of outpatient appointments by family doctors with a significance level of 0.05 was tested using statistical data analysis according to the Student's test. The presented results of the analysis of a medical experiment with general practitioners allowed drawing a conclusion about the positive impact of the use of mobile applications on the working time savings of general practitioners when providing medical services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".